{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T18:04:26.406407Z",
     "start_time": "2019-05-13T18:04:26.395100Z"
    }
   },
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:47.484046Z",
     "start_time": "2019-06-18T15:17:47.441124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appended C:\\Users\\black\\PycharmProjects\\active_learning\\active_learning\\src to paths\n",
      "Switched to directory C:\\Users\\black\\PycharmProjects\\active_learning\\active_learning\n",
      "%load_ext autoreload\n",
      "%autoreload 2\n"
     ]
    }
   ],
   "source": [
    "import blackhc.notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:48.180801Z",
     "start_time": "2019-06-18T15:17:47.679194Z"
    }
   },
   "outputs": [],
   "source": [
    "import al_notebook.results_loader as rl\n",
    "import al_notebook.plots as alp\n",
    "from acquisition_functions import AcquisitionFunction\n",
    "from acquisition_method import AcquisitionMethod\n",
    "from dataset_enum import DatasetEnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:48.190720Z",
     "start_time": "2019-06-18T15:17:48.182113Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:48.324276Z",
     "start_time": "2019-06-18T15:17:48.304186Z"
    }
   },
   "outputs": [],
   "source": [
    "import prettyprinter as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:48.565435Z",
     "start_time": "2019-06-18T15:17:48.553972Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:48.837258Z",
     "start_time": "2019-06-18T15:17:48.822600Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:50.727107Z",
     "start_time": "2019-06-18T15:17:50.703207Z"
    }
   },
   "outputs": [],
   "source": [
    "#MARKERS = (\"X\", \"s\", \"o\")\n",
    "MARKERS = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:52.654492Z",
     "start_time": "2019-06-18T15:17:52.619795Z"
    }
   },
   "outputs": [],
   "source": [
    "def acc_label_axes():\n",
    "    plt.xlabel('Acquired dataset size')\n",
    "    plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:59.148517Z",
     "start_time": "2019-06-18T15:17:53.657034Z"
    }
   },
   "outputs": [],
   "source": [
    "stores = rl.load_experiment_results('paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:59.160586Z",
     "start_time": "2019-06-18T15:17:59.149636Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "al_notebook.results_loader._args(\n",
      "    batch_size=64,\n",
      "    scoring_batch_size=1000,\n",
      "    test_batch_size=1000,\n",
      "    validation_set_size=1000,\n",
      "    early_stopping_patience=3,\n",
      "    epochs=30,\n",
      "    epoch_samples=5056,\n",
      "    num_inference_samples=20,\n",
      "    available_sample_k=10,\n",
      "    num_iterations=100,\n",
      "    no_cuda=False,\n",
      "    name='bald_20_1023109',\n",
      "    seed=1023109,\n",
      "    log_interval=10,\n",
      "    type=acquisition_functions.AcquisitionFunction.bald,\n",
      "    acquisition_method=acquisition_method.AcquisitionMethod.independent,\n",
      "    dataset=dataset_enum.DatasetEnum.mnist\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(rl.get_any(stores).args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T15:17:59.190393Z",
     "start_time": "2019-06-18T15:17:59.161671Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'num_inference_samples': {100, 10, 20},\n",
      "    'available_sample_k': {40, 1, 10, 5},\n",
      "    'num_iterations': {100, 20, None},\n",
      "    'type': {\n",
      "        acquisition_functions.AcquisitionFunction.random,\n",
      "        acquisition_functions.AcquisitionFunction.mean_stddev,\n",
      "        acquisition_functions.AcquisitionFunction.variation_ratios,\n",
      "        acquisition_functions.AcquisitionFunction.bald\n",
      "    },\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'dataset': {\n",
      "        dataset_enum.DatasetEnum.repeated_mnist_w_noise,\n",
      "        dataset_enum.DatasetEnum.emnist,\n",
      "        dataset_enum.DatasetEnum.mnist\n",
      "    },\n",
      "    'num_acquired_points': {\n",
      "        260,\n",
      "        263,\n",
      "        267,\n",
      "        270,\n",
      "        275,\n",
      "        276,\n",
      "        280,\n",
      "        287,\n",
      "        288,\n",
      "        297,\n",
      "        298,\n",
      "        300,\n",
      "        820,\n",
      "        320,\n",
      "        196,\n",
      "        208,\n",
      "        340,\n",
      "        215,\n",
      "        240,\n",
      "        245,\n",
      "        246,\n",
      "        1020,\n",
      "        255\n",
      "    },\n",
      "    'num_initial_samples': {0, 20},\n",
      "    'experiment_description': {\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD and '\n",
      "            'heuristic',\n",
      "        'EMNIST with b5 and k10, k100 with both BALD and BatchBALD',\n",
      "        'Reproduce b1, 5, 10 k10, 100 and default initial samples, no initial '\n",
      "            'samples for all methods with BALD. (No culling!)',\n",
      "        'RMNIST with noise k100 var ratios and mean stddev',\n",
      "        'EMNIST_balanced with random acquisition',\n",
      "        'Additional paper experiments (#1): RMNIST w/ noise, MNIST w/ noise, '\n",
      "            'MNIST, with initial samples',\n",
      "        'Coreset BALD vs BALD',\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), random acquisition'\n",
      "    },\n",
      "    'initial_samples': {\n",
      "        (),\n",
      "        (\n",
      "            38043,\n",
      "            40091,\n",
      "            17418,\n",
      "            2094,\n",
      "            39879,\n",
      "            3133,\n",
      "            5011,\n",
      "            40683,\n",
      "            54379,\n",
      "            24287,\n",
      "            9849,\n",
      "            59305,\n",
      "            39508,\n",
      "            39356,\n",
      "            8758,\n",
      "            52579,\n",
      "            13655,\n",
      "            7636,\n",
      "            21562,\n",
      "            41329\n",
      "        )\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/rmnist_w_noise_other_methods/configs.py',\n",
      "        './experiment_configs/emnist_random/configs.py',\n",
      "        './experiment_configs/rmnist_noise_random/configs.py',\n",
      "        './experiment_configs/emnist_bbb/configs.py',\n",
      "        './experiment_configs/big_repro_b10_k100/configs.py',\n",
      "        './experiment_configs/paper_exp1/configs.py',\n",
      "        './experiment_configs/rmnist_w_noise/configs.py',\n",
      "        './experiment_configs/coreset_bald_vs_bald/configs.py'\n",
      "    },\n",
      "    'initial_percentage': {50, 100, None},\n",
      "    'reduce_percentage': {0, 10, None},\n",
      "    'min_remaining_percentage': {100, None, 30},\n",
      "    'min_candidates_per_acquired_item': {100, 20, None},\n",
      "    'balanced_validation_set': {False, None}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(rl.diff_args(stores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-14T14:51:44.438647Z",
     "start_time": "2019-05-14T14:51:44.400116Z"
    }
   },
   "source": [
    "### Increasing acquisition size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:44:15.249600Z",
     "start_time": "2019-06-13T11:44:14.736851Z"
    },
    "run_control": {
     "marked": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {40, 1, 10},\n",
      "    'num_iterations': {100, 20, None},\n",
      "    'num_acquired_points': {263, 267, 208, 820, 245, 246, 276, 1020}\n",
      "}\n",
      "{\n",
      "    'Batch acquisition size 10': {\n",
      "        'num_trials': 12,\n",
      "        'num_inference_samples': {20},\n",
      "        'available_sample_k': {10},\n",
      "        'num_iterations': {100},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'num_acquired_points': {1020},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Batch acquisition size 40': {\n",
      "        'num_trials': 12,\n",
      "        'num_inference_samples': {20},\n",
      "        'available_sample_k': {40},\n",
      "        'num_iterations': {20},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'num_acquired_points': {820},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Batch acquisition size 1': {\n",
      "        'num_trials': 6,\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'experiment_description': {'Coreset BALD vs BALD'},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'available_sample_k': {1},\n",
      "        'num_inference_samples': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/coreset_bald_vs_bald/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {263, 267, 208, 276, 245, 246},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "Batch acquisition size 1:\n",
      "90% at [79 86 93]\n",
      "95% at [184 190 212]\n",
      "Batch acquisition size 10:\n",
      "90% at [110 130 140]\n",
      "95% at [230 230 240]\n",
      "Batch acquisition size 40:\n",
      "90% at [140 180 180]\n",
      "95% at [260 300 300]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered = {}\n",
    "\n",
    "def filter_exps(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    if vip_args.ds != DatasetEnum.mnist:\n",
    "        return False\n",
    "\n",
    "    if vip_args.nis == 0:\n",
    "        return False\n",
    "\n",
    "    if vip_args.am != AcquisitionMethod.independent:\n",
    "        return False\n",
    "\n",
    "    if vip_args.af != AcquisitionFunction.bald:\n",
    "        return False\n",
    "\n",
    "    if vip_args.k not in (20, ):\n",
    "        return False\n",
    "\n",
    "    return vip_args.b in (1, 10, 40)\n",
    "\n",
    "    return False\n",
    "\n",
    "\n",
    "filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "def key2text(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    return f'Batch acquisition size {vip_args.b}'\n",
    "\n",
    "\n",
    "grouped_by = rl.groupby_dict(\n",
    "    filtered,\n",
    "    key_kv=key2text)\n",
    "\n",
    "pp.pprint(rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "          \n",
    "grouped_by = rl.map_dict(grouped_by, v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(90, 95)))\n",
    "\n",
    "sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "#plt.title(\"BALD for increasing acquisition size\")\n",
    "alp.plot_aggregated_groups(sorted_dict,\n",
    "                           show_num_trials=False,\n",
    "                           show_quantiles=False, markers=MARKERS)\n",
    "plt.axis([20, 260, 0.65, 0.98])\n",
    "acc_label_axes()\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "output_path = blackhc.notebook.original_dir + '/BALD_inc_acq_size.png'\n",
    "alp.plot_save(output_path, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BALD vs BatchBALD on MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:44:20.790801Z",
     "start_time": "2019-06-13T11:44:20.264209Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {1, 10},\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'initial_percentage': {100, None},\n",
      "    'reduce_percentage': {0, None},\n",
      "    'num_acquired_points': {288, 196, 260, 297, 298, 300, 270, 245, 287}\n",
      "}\n",
      "dict_keys(['BALD acquisition size 10', 'BALD acquisition size 1', 'BatchBALD acquisition size 10'])\n",
      "{\n",
      "    'BALD acquisition size 10': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_independent_bald_k100_b10_1029338.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_1038804.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_572400.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_661442.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_734490.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {10},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD acquisition size 1': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_independent_bald_k100_b1_1029338.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_1038804.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_572400.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_661442.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_734490.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {1},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100, None},\n",
      "        'reduce_percentage': {0, None},\n",
      "        'num_acquired_points': {288, 196, 297, 298, 245, 287},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD acquisition size 10': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_multibald_bald_k100_b10_1029338.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_1038804.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_572400.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_661442.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_734490.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {10},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {300, 260, 270},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD acquisition size 10:\n",
      "90% at [ 70  90 110]\n",
      "95% at [190 200 230]\n",
      "BALD acquisition size 1:\n",
      "90% at [ 85  96 101]\n",
      "95% at [197 206 226]\n",
      "BALD acquisition size 10:\n",
      "90% at [120. 120. 170.]\n",
      "95% at [250. 250.  inf]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%capture c\n",
    "#c=None\n",
    "\n",
    "filtered = {}\n",
    "\n",
    "def filter_exps(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    if vip_args.ds != DatasetEnum.mnist:\n",
    "        return False\n",
    "\n",
    "    if vip_args.nis == 0:\n",
    "        return False\n",
    "      \n",
    "    if vip_args.nap < 100:\n",
    "        return False\n",
    "\n",
    "    if vip_args.am not in (AcquisitionMethod.multibald, AcquisitionMethod.independent):\n",
    "        return False\n",
    "    \n",
    "    if vip_args.am == AcquisitionMethod.independent and vip_args.af not in (AcquisitionFunction.bald, AcquisitionFunction.random):\n",
    "        return False\n",
    "    \n",
    "    if (vip_args.am, vip_args.af) == (AcquisitionMethod.independent, AcquisitionFunction.random):\n",
    "        return False\n",
    "\n",
    "    if (vip_args.am, vip_args.af) == (AcquisitionMethod.independent, AcquisitionFunction.bald):\n",
    "        if vip_args.b == 1:\n",
    "            if vip_args.k != 100:\n",
    "                return False\n",
    "        else:\n",
    "            if vip_args.k not in (100,):\n",
    "                return False\n",
    "       \n",
    "    if vip_args.am == AcquisitionMethod.multibald:\n",
    "        if vip_args.k != 100:\n",
    "            return False\n",
    "\n",
    "    return vip_args.b in (1, 10)\n",
    "    \n",
    "\n",
    "\n",
    "filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "\n",
    "def key2text(name, store):    \n",
    "    vip_args = rl.get_vip_args(store)\n",
    "    am, af = vip_args.am, vip_args.af\n",
    "    if (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.random):\n",
    "        return 'Random acquisition'\n",
    "    elif (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.bald):\n",
    "        return f'BALD acquisition size {vip_args.b}'\n",
    "    elif am == AcquisitionMethod.multibald:\n",
    "        return f'BatchBALD acquisition size {vip_args.b}'\n",
    "    raise ValueError(vip_args)\n",
    "\n",
    "\n",
    "grouped_by = rl.groupby_dict(\n",
    "    filtered,\n",
    "    key_kv=key2text)\n",
    "\n",
    "pp.pprint(grouped_by.keys())\n",
    "\n",
    "pp.pprint(rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores, True)))\n",
    "          \n",
    "grouped_by = rl.map_dict(grouped_by, v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(90, 95)))\n",
    "\n",
    "sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "#plt.title(\"MNIST BALD and BatchBALD comparison\")\n",
    "alp.plot_aggregated_groups(sorted_dict,\n",
    "                           show_num_trials=False,\n",
    "                           show_quantiles=False, markers=MARKERS)\n",
    "plt.axis([20, 260, 0.65, 0.98])\n",
    "acc_label_axes()\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "output_path = blackhc.notebook.original_dir + '/MNIST_BALD_BATCHBALD.png'\n",
    "alp.plot_save(output_path, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BatchBALD with increasing b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:44:30.916301Z",
     "start_time": "2019-06-13T11:44:30.421132Z"
    },
    "hide_input": false,
    "hide_output": false,
    "run_control": {
     "marked": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {40, 10, 5},\n",
      "    'experiment_description': {\n",
      "        'Additional paper experiments (#1): RMNIST w/ noise, MNIST w/ noise, '\n",
      "            'MNIST, with initial samples',\n",
      "        'Reproduce b1, 5, 10 k10, 100 and default initial samples, no initial '\n",
      "            'samples for all methods with BALD. (No culling!)'\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/big_repro_b10_k100/configs.py',\n",
      "        './experiment_configs/paper_exp1/configs.py'\n",
      "    },\n",
      "    'num_acquired_points': {260, 300, 270, 240, 275, 340, 245, 215, 255}\n",
      "}\n",
      "{\n",
      "    'Batch acquisition size 10': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {10},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {300, 260, 270},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Batch acquisition size 40': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {40},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'Additional paper experiments (#1): RMNIST w/ noise, MNIST w/ '\n",
      "            'noise, MNIST, with initial samples'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'experiments_laaos': {'./experiment_configs/paper_exp1/configs.py'},\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {340},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Batch acquisition size 5': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {5},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {240, 275, 245, 215, 255},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "Batch acquisition size 5:\n",
      "90% at [ 85  85 100]\n",
      "95% at [170 185 215]\n",
      "Batch acquisition size 10:\n",
      "90% at [ 70  90 110]\n",
      "95% at [190 200 230]\n",
      "Batch acquisition size 40:\n",
      "90% at [100. 100. 140.]\n",
      "95% at [220. 260. 260.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered = {}\n",
    "\n",
    "def filter_exps(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    if vip_args.ds not in (\n",
    "            DatasetEnum.mnist, ):  #, DatasetEnum.mnist_w_noise):\n",
    "        return False\n",
    "\n",
    "    if vip_args.nis == 0:\n",
    "        return False\n",
    "    \n",
    "    if vip_args.nap < 100:\n",
    "        return False\n",
    "\n",
    "    if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "                                           AcquisitionFunction.bald),):\n",
    "        return False\n",
    "    \n",
    "    if vip_args.k != 100:\n",
    "        return False\n",
    "    \n",
    "    return True\n",
    "\n",
    "filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "\n",
    "def key2text(name, store):\n",
    "\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "    am, af = vip_args.am, vip_args.af\n",
    "    if (am, af) == (AcquisitionMethod.multibald, AcquisitionFunction.bald):\n",
    "        return f'Batch acquisition size {vip_args.b}'\n",
    "    raise ValueError(vip_args)\n",
    "\n",
    "\n",
    "grouped_by = rl.groupby_dict(filtered,\n",
    "                             key_kv=rl.get_diff_args_key2text(\n",
    "                                 filtered, ['tag'] + rl.culling_fields))\n",
    "\n",
    "grouped_by = rl.groupby_dict(filtered,\n",
    "                             key_kv=key2text)\n",
    "\n",
    "\n",
    "pp.pprint(rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "grouped_by = rl.map_dict(\n",
    "    grouped_by,\n",
    "    v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(90, 95)))\n",
    "\n",
    "sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "#plt.title(\"BatchBALD for increasing acquisition size\")\n",
    "alp.plot_aggregated_groups(sorted_dict,\n",
    "                           show_num_trials=False,\n",
    "                           show_quantiles=False)\n",
    "plt.axis([20, 260, 0.65, 0.98])\n",
    "acc_label_axes()\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "output_path = blackhc.notebook.original_dir + '/BatchBALD_increasing_b.png'\n",
    "alp.plot_save(output_path, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time spent on MNIST BALD b1 vs BatchBALD b10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-25T00:32:00.084818Z",
     "start_time": "2019-05-25T00:32:00.073306Z"
    }
   },
   "outputs": [],
   "source": [
    "batchbalds_b10 = [\n",
    "            'paper/mnist_multibald_bald_k100_b10_661442.py',\n",
    "            'paper/mnist_multibald_bald_k100_b10_572400.py',\n",
    "            'paper/mnist_multibald_bald_k100_b10_1038804.py',\n",
    "            'paper/mnist_multibald_bald_k100_b10_926965.py',\n",
    "            'paper/mnist_multibald_bald_k100_b10_734490.py',\n",
    "            'paper/mnist_multibald_bald_k100_b10_1029338.py'\n",
    "        ]\n",
    "balds_b1 = [\n",
    "            'paper/mnist_independent_bald_k100_b1_926965.py',\n",
    "            'paper/mnist_independent_bald_k100_b1_661442.py',\n",
    "            'paper/mnist_independent_bald_k100_b1_734490.py',\n",
    "            'paper/mnist_independent_bald_k100_b1_1038804.py',\n",
    "            'paper/mnist_independent_bald_k100_b1_572400.py',\n",
    "            'paper/mnist_independent_bald_k100_b1_1029338.py'\n",
    "        ],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-25T00:46:06.224178Z",
     "start_time": "2019-05-25T00:46:05.748096Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {1, 10},\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'initial_percentage': {100, None},\n",
      "    'reduce_percentage': {0, None},\n",
      "    'num_acquired_points': {288, 196, 260, 297, 298, 300, 270, 245, 287}\n",
      "}\n",
      "dict_keys(['BALD acquisition size 10', 'BALD acquisition size 1', 'BatchBALD acquisition size 10'])\n",
      "{\n",
      "    'BALD acquisition size 10': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_independent_bald_k100_b10_1029338.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_1038804.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_572400.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_661442.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_734490.py',\n",
      "            'paper/mnist_independent_bald_k100_b10_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {10},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD acquisition size 1': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_independent_bald_k100_b1_1029338.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_1038804.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_572400.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_661442.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_734490.py',\n",
      "            'paper/mnist_independent_bald_k100_b1_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {1},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100, None},\n",
      "        'reduce_percentage': {0, None},\n",
      "        'num_acquired_points': {288, 196, 297, 298, 245, 287},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD acquisition size 10': {\n",
      "        'num_trials': 6,\n",
      "        'keys': [\n",
      "            'paper/mnist_multibald_bald_k100_b10_1029338.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_1038804.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_572400.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_661442.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_734490.py',\n",
      "            'paper/mnist_multibald_bald_k100_b10_926965.py'\n",
      "        ],\n",
      "        'num_inference_samples': {100},\n",
      "        'available_sample_k': {10},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'Reproduce b1, 5, 10 k10, 100 and default initial samples, no '\n",
      "            'initial samples for all methods with BALD. (No culling!)'\n",
      "        },\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {20},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/big_repro_b10_k100/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'num_acquired_points': {300, 260, 270},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BALD acquisition size 1:\n",
      "BatchBALD acquisition size 10:\n",
      "BALD acquisition size 10:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%capture c\n",
    "#c=None\n",
    "\n",
    "filtered = {}\n",
    "\n",
    "\n",
    "def filter_exps(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    if vip_args.ds != DatasetEnum.mnist:\n",
    "        return False\n",
    "\n",
    "    if vip_args.nis == 0:\n",
    "        return False\n",
    "\n",
    "    if vip_args.nap < 100:\n",
    "        return False\n",
    "\n",
    "    if vip_args.am not in (AcquisitionMethod.multibald,\n",
    "                           AcquisitionMethod.independent):\n",
    "        return False\n",
    "\n",
    "    if vip_args.am == AcquisitionMethod.independent and vip_args.af not in (\n",
    "            AcquisitionFunction.bald, AcquisitionFunction.random):\n",
    "        return False\n",
    "\n",
    "    if (vip_args.am, vip_args.af) == (AcquisitionMethod.independent,\n",
    "                                      AcquisitionFunction.random):\n",
    "        return False\n",
    "\n",
    "    if (vip_args.am, vip_args.af) == (AcquisitionMethod.independent,\n",
    "                                      AcquisitionFunction.bald):\n",
    "        if vip_args.tag in ('repro', 'k20', 'k40', 'k5', 'k1',\n",
    "                            'coreset_bald_vs_bald', 'bald_b5_k20'):\n",
    "            return False\n",
    "\n",
    "        if vip_args.b == 1:\n",
    "            if vip_args.k != 100:\n",
    "                return False\n",
    "        else:\n",
    "            if vip_args.k not in (100, ):\n",
    "                return False\n",
    "\n",
    "    if vip_args.am == AcquisitionMethod.multibald:\n",
    "        if store.tag == 'k5':\n",
    "            return False\n",
    "        if vip_args.k != 100:\n",
    "            return False\n",
    "\n",
    "    return vip_args.b in (1, 10)\n",
    "\n",
    "\n",
    "filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "\n",
    "def key2text(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "    am, af = vip_args.am, vip_args.af\n",
    "    if (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.random):\n",
    "        return 'Random acquisition'\n",
    "    elif (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.bald):\n",
    "        return f'BALD acquisition size {vip_args.b}'\n",
    "    elif am == AcquisitionMethod.multibald:\n",
    "        return f'BatchBALD acquisition size {vip_args.b}'\n",
    "    raise ValueError(vip_args)\n",
    "\n",
    "\n",
    "grouped_by = rl.groupby_dict(filtered, key_kv=key2text)\n",
    "\n",
    "pp.pprint(grouped_by.keys())\n",
    "\n",
    "pp.pprint(\n",
    "    rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores, True)))\n",
    "\n",
    "\n",
    "def values_getter(store):\n",
    "    #return np.cumsum([iteration.train_model_elapsed_time for iteration in store.iterations])\n",
    "    return np.cumsum([\n",
    "        iteration.train_model_elapsed_time +\n",
    "        iteration.batch_acquisition_elapsed_time\n",
    "        for iteration in store.iterations\n",
    "    ])\n",
    "\n",
    "\n",
    "grouped_by_absolute = rl.map_dict(\n",
    "    grouped_by,\n",
    "    v=lambda stores: rl.aggregate_values(stores, values_getter, thresholds=()))\n",
    "\n",
    "rel_time_unit = grouped_by_absolute['BatchBALD acquisition size 10'].quantiles[\n",
    "    1, 19]\n",
    "\n",
    "\n",
    "def values_getter_rel(store):\n",
    "    #return np.cumsum([iteration.train_model_elapsed_time for iteration in store.iterations])\n",
    "    return np.cumsum([\n",
    "        iteration.train_model_elapsed_time +\n",
    "        iteration.batch_acquisition_elapsed_time\n",
    "        for iteration in store.iterations\n",
    "    ]) / rel_time_unit\n",
    "\n",
    "\n",
    "grouped_by_relative = rl.map_dict(\n",
    "    grouped_by,\n",
    "    v=lambda stores: rl.aggregate_values(\n",
    "        stores, values_getter_rel, thresholds=()))\n",
    "\n",
    "sorted_dict = {\n",
    "    k: grouped_by_relative[k]\n",
    "    for k in [\n",
    "        'BALD acquisition size 1',\n",
    "        'BatchBALD acquisition size 10',\n",
    "        'BALD acquisition size 10',\n",
    "    ]\n",
    "}\n",
    "\n",
    "#tmp = alp.COLOR_ORDER\n",
    "#alp.COLOR_ORDER = [tmp[1], tmp[0], tmp[2]]\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "#plt.title(\"MNIST BALD and BatchBALD comparison\")\n",
    "alp.plot_aggregated_groups(sorted_dict,\n",
    "                           show_num_trials=False,\n",
    "                           show_quantiles=False,\n",
    "                           markers=MARKERS)\n",
    "\n",
    "plt.scatter(206,\n",
    "            sorted_dict['BALD acquisition size 1'].quantiles[1, 187],\n",
    "            marker='x', s=100, \n",
    "            color=alp.COLOR_ORDER[0])\n",
    "plt.scatter(200,\n",
    "            sorted_dict['BatchBALD acquisition size 10'].quantiles[1, 19],\n",
    "            marker='x', s=100,\n",
    "            color=alp.COLOR_ORDER[1])\n",
    "plt.scatter(250,\n",
    "            sorted_dict['BALD acquisition size 10'].quantiles[1, 24],\n",
    "            marker='x', s=100,\n",
    "            color=alp.COLOR_ORDER[2])\n",
    "\n",
    "#alp.COLOR_ORDER = tmp\n",
    "\n",
    "plt.axis([20, 260, 0, 10])\n",
    "plt.xlabel('Acquired dataset size')\n",
    "plt.ylabel('Relative total time')\n",
    "plt.yticks(ticks=range(11))\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "output_path = blackhc.notebook.original_dir + '/MNIST_BALD_BATCHBALD_time_perf.png'\n",
    "alp.plot_save(output_path, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-22T17:57:44.506738Z",
     "start_time": "2019-05-22T17:57:44.493892Z"
    }
   },
   "source": [
    "### RMNIST vs BALD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:45:03.327102Z",
     "start_time": "2019-06-13T11:45:02.835819Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {10, 5},\n",
      "    'type': {\n",
      "        acquisition_functions.AcquisitionFunction.random,\n",
      "        acquisition_functions.AcquisitionFunction.bald\n",
      "    },\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'experiment_description': {\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), random acquisition',\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD and '\n",
      "            'heuristic'\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/rmnist_noise_random/configs.py',\n",
      "        './experiment_configs/rmnist_w_noise/configs.py'\n",
      "    },\n",
      "    'num_acquired_points': {280, 300, 260}\n",
      "}\n",
      "{\n",
      "    'BALD': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Random': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {5},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.random},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), random '\n",
      "            'acquisition'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_noise_random/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {280, 300, 260},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD:\n",
      "90% at [120. 120. 130.]\n",
      "95% at [260. 260. 280.]\n",
      "Random:\n",
      "90% at [185. 195. 225.]\n",
      "95% at [inf inf inf]\n",
      "BALD:\n",
      "90% at [260. 280.  inf]\n",
      "95% at [inf inf inf]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for show_b in (10,):\n",
    "    filtered = {}\n",
    "    \n",
    "    def filter_exps(name, store):\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "\n",
    "        if vip_args.ds not in (DatasetEnum.repeated_mnist_w_noise,): #, DatasetEnum.mnist_w_noise):\n",
    "            return False\n",
    "\n",
    "        if vip_args.nis == 0:\n",
    "            return False\n",
    "\n",
    "\n",
    "    #     if vip_args.nap < 300:\n",
    "    #         return False\n",
    "\n",
    "        if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              (AcquisitionMethod.independent,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              (AcquisitionMethod.independent, AcquisitionFunction.random)):\n",
    "            return False\n",
    "\n",
    "        if vip_args.af == AcquisitionFunction.bald:\n",
    "            if vip_args.k not in (10,):\n",
    "                return False\n",
    "\n",
    "            if vip_args.b not in (show_b,):\n",
    "                return False\n",
    "        return True\n",
    "\n",
    "    filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "    pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "\n",
    "    def key2text(name, store):\n",
    "\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "        am, af = vip_args.am, vip_args.af\n",
    "        if (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.random):\n",
    "            return 'Random'\n",
    "        elif (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.bald):\n",
    "            return f'BALD'\n",
    "        elif (am, af) == (AcquisitionMethod.multibald, AcquisitionFunction.bald):\n",
    "            return f'BatchBALD'\n",
    "        raise ValueError(vip_args)\n",
    "\n",
    "\n",
    "    #grouped_by = rl.groupby_dict(filtered, key_kv=rl.get_diff_args_key2text(filtered, ('tag',)))\n",
    "    grouped_by = rl.groupby_dict(filtered, key_kv=key2text)\n",
    "\n",
    "    pp.pprint(rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "    grouped_by = rl.map_dict(\n",
    "        grouped_by,\n",
    "        v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(90, 95)))\n",
    "\n",
    "    sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    #plt.title(\"RMNIST\")\n",
    "    alp.plot_aggregated_groups(sorted_dict,\n",
    "                               show_num_trials=False,\n",
    "                               show_quantiles=False,\n",
    "                               show_thresholds=False)\n",
    "    plt.axis([50, 280, 0.7, 0.98])\n",
    "    acc_label_axes()\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc='lower right')\n",
    "\n",
    "    output_path = blackhc.notebook.original_dir + '/RMNIST.png'\n",
    "    alp.plot_save(output_path, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:45:11.385014Z",
     "start_time": "2019-06-13T11:45:10.815340Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'available_sample_k': {10, 5},\n",
      "    'type': {\n",
      "        acquisition_functions.AcquisitionFunction.random,\n",
      "        acquisition_functions.AcquisitionFunction.mean_stddev,\n",
      "        acquisition_functions.AcquisitionFunction.variation_ratios,\n",
      "        acquisition_functions.AcquisitionFunction.bald\n",
      "    },\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'experiment_description': {\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), random acquisition',\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD and '\n",
      "            'heuristic',\n",
      "        'RMNIST with noise k100 var ratios and mean stddev'\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/rmnist_w_noise_other_methods/configs.py',\n",
      "        './experiment_configs/rmnist_noise_random/configs.py',\n",
      "        './experiment_configs/rmnist_w_noise/configs.py'\n",
      "    },\n",
      "    'num_acquired_points': {320, 280, 300, 260},\n",
      "    'balanced_validation_set': {False, None}\n",
      "}\n",
      "{\n",
      "    'BALD': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Mean STD': {\n",
      "        'num_trials': 5,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.mean_stddev},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k100 var ratios and mean stddev'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise_other_methods/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'balanced_validation_set': {False},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Random': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {5},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.random},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), random '\n",
      "            'acquisition'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_noise_random/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'Var ratios': {\n",
      "        'num_trials': 2,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.variation_ratios},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k100 var ratios and mean stddev'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise_other_methods/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'balanced_validation_set': {False},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {280, 300, 260},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD:\n",
      "90% at [120. 120. 130.]\n",
      "95% at [260. 260. 280.]\n",
      "Random:\n",
      "90% at [185. 195. 225.]\n",
      "95% at [inf inf inf]\n",
      "BALD:\n",
      "90% at [260. 280.  inf]\n",
      "95% at [inf inf inf]\n",
      "Var ratios:\n",
      "90% at [110. 110. 230.]\n",
      "95% at [inf inf inf]\n",
      "Mean STD:\n",
      "90% at [180. 190. 200.]\n",
      "95% at [inf inf inf]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for show_b in (10, ):\n",
    "    filtered = {}\n",
    "\n",
    "    def filter_exps(name, store):\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "\n",
    "        if vip_args.ds not in (DatasetEnum.repeated_mnist_w_noise,\n",
    "                               ):  #, DatasetEnum.mnist_w_noise):\n",
    "            return False\n",
    "\n",
    "        if vip_args.nis == 0:\n",
    "            return False\n",
    "\n",
    "    #     if vip_args.nap < 300:\n",
    "    #         return False\n",
    "\n",
    "#         if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "#                                                AcquisitionFunction.bald),\n",
    "#                                               (AcquisitionMethod.independent,\n",
    "#                                                AcquisitionFunction.bald),\n",
    "#                                               (AcquisitionMethod.independent, AcquisitionFunction.random)):\n",
    "#             return False\n",
    "\n",
    "        if vip_args.af != AcquisitionFunction.random:\n",
    "            if vip_args.k not in (10, ):\n",
    "                return False\n",
    "\n",
    "            if vip_args.b not in (show_b, ):\n",
    "                return False\n",
    "        return True\n",
    "\n",
    "    filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "    pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "    def key2text(name, store):\n",
    "\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "        am, af = vip_args.am, vip_args.af\n",
    "        if (am, af) == (AcquisitionMethod.independent,\n",
    "                        AcquisitionFunction.random):\n",
    "            return 'Random'\n",
    "        elif (am, af) == (AcquisitionMethod.independent,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return f'BALD'\n",
    "        elif (am, af) == (AcquisitionMethod.independent,\n",
    "                          AcquisitionFunction.variation_ratios):\n",
    "            return f'Var ratios'\n",
    "        elif (am, af) == (AcquisitionMethod.independent,\n",
    "                          AcquisitionFunction.mean_stddev):\n",
    "            return f'Mean STD'\n",
    "        elif (am, af) == (AcquisitionMethod.multibald,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return f'BatchBALD'\n",
    "        raise ValueError(vip_args)\n",
    "\n",
    "    #grouped_by = rl.groupby_dict(filtered, key_kv=rl.get_diff_args_key2text(filtered, ('tag',)))\n",
    "    grouped_by = rl.groupby_dict(filtered, key_kv=key2text)\n",
    "\n",
    "    pp.pprint(\n",
    "        rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "    grouped_by = rl.map_dict(\n",
    "        grouped_by,\n",
    "        v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(90, 95)))\n",
    "\n",
    "    #sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "    sorted_dict = {\n",
    "        key: grouped_by[key]\n",
    "        for key in ['BatchBALD', 'Random', 'BALD', 'Var ratios', 'Mean STD']\n",
    "    }\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    #plt.title(\"RMNIST\")\n",
    "    alp.plot_aggregated_groups(sorted_dict,\n",
    "                               show_num_trials=False,\n",
    "                               show_quantiles=False,\n",
    "                               show_thresholds=False)\n",
    "    plt.axis([50, 280, 0.7, 0.98])\n",
    "    acc_label_axes()\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc='lower right')\n",
    "\n",
    "    output_path = blackhc.notebook.original_dir + '/RMNIST_others.png'\n",
    "    alp.plot_save(output_path, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-13T11:45:43.509928Z",
     "start_time": "2019-06-13T11:45:43.048337Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'num_trials': 18,\n",
      "    'num_inference_samples': {10},\n",
      "    'available_sample_k': {5},\n",
      "    'type': {\n",
      "        acquisition_functions.AcquisitionFunction.random,\n",
      "        acquisition_functions.AcquisitionFunction.bald\n",
      "    },\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.independent,\n",
      "        acquisition_method.AcquisitionMethod.multibald\n",
      "    },\n",
      "    'experiment_description': {\n",
      "        'EMNIST_balanced with random acquisition',\n",
      "        'EMNIST with b5 and k10, k100 with both BALD and BatchBALD'\n",
      "    },\n",
      "    'initial_percentage': {50, None},\n",
      "    'reduce_percentage': {10, None},\n",
      "    'min_remaining_percentage': {None, 30},\n",
      "    'min_candidates_per_acquired_item': {100, None},\n",
      "    'dataset': {dataset_enum.DatasetEnum.emnist},\n",
      "    'initial_samples': {()},\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/emnist_bbb/configs.py',\n",
      "        './experiment_configs/emnist_random/configs.py'\n",
      "    },\n",
      "    'quickquick': {False},\n",
      "    'initial_samples_per_class': {2},\n",
      "    'num_acquired_points': {300},\n",
      "    'num_initial_samples': {0},\n",
      "    'tag': {'paper'}\n",
      "}\n",
      "{\n",
      "    'Random': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {5},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.random},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {'EMNIST_balanced with random acquisition'},\n",
      "        'initial_percentage': {50},\n",
      "        'reduce_percentage': {10},\n",
      "        'min_remaining_percentage': {30},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.emnist},\n",
      "        'initial_samples': {()},\n",
      "        'experiments_laaos': {'./experiment_configs/emnist_random/configs.py'},\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {0},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD': {\n",
      "        'num_trials': 6,\n",
      "        'available_sample_k': {5},\n",
      "        'num_inference_samples': {10},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'EMNIST with b5 and k10, k100 with both BALD and BatchBALD'\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'dataset': {dataset_enum.DatasetEnum.emnist},\n",
      "        'initial_samples': {()},\n",
      "        'experiments_laaos': {'./experiment_configs/emnist_bbb/configs.py'},\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {0},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD': {\n",
      "        'num_trials': 6,\n",
      "        'available_sample_k': {5},\n",
      "        'num_inference_samples': {10},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'EMNIST with b5 and k10, k100 with both BALD and BatchBALD'\n",
      "        },\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'dataset': {dataset_enum.DatasetEnum.emnist},\n",
      "        'initial_samples': {()},\n",
      "        'experiments_laaos': {'./experiment_configs/emnist_bbb/configs.py'},\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {0},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD:\n",
      "40% at [160 165 175]\n",
      "45% at [210 215 225]\n",
      "Random:\n",
      "40% at [175 175 200]\n",
      "45% at [215 230 260]\n",
      "BALD:\n",
      "40% at [215. 220. 230.]\n",
      "45% at [260. 280. 285.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered = {}\n",
    "\n",
    "\n",
    "def filter_exps(name, store):\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "\n",
    "    if vip_args.ds not in (\n",
    "            DatasetEnum.emnist, ):  #, DatasetEnum.mnist_w_noise):\n",
    "        return False\n",
    "\n",
    "    if vip_args.nis != 0:\n",
    "        return False\n",
    "    \n",
    "    if vip_args.nap < 100:\n",
    "        return False\n",
    "\n",
    "#     if vip_args.nap < 300:\n",
    "#         return False\n",
    "\n",
    "    if vip_args.k != 10:\n",
    "        return False\n",
    "\n",
    "    if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "                                           AcquisitionFunction.bald),\n",
    "                                          (AcquisitionMethod.independent,\n",
    "                                           AcquisitionFunction.bald),\n",
    "                                          (AcquisitionMethod.independent,\n",
    "                                           AcquisitionFunction.random)):\n",
    "        return False\n",
    "\n",
    "    \n",
    "    return True\n",
    "\n",
    "filtered.update(rl.filter_dict(stores, kv=filter_exps))\n",
    "\n",
    "pp.pprint(rl.get_stores_info(filtered))\n",
    "\n",
    "\n",
    "def key2text(name, store):\n",
    "\n",
    "    vip_args = rl.get_vip_args(store)\n",
    "    am, af = vip_args.am, vip_args.af\n",
    "    if (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.random):\n",
    "        return 'Random'\n",
    "    elif (am, af) == (AcquisitionMethod.independent, AcquisitionFunction.bald):\n",
    "        return f'BALD'\n",
    "    if (am, af) == (AcquisitionMethod.multibald, AcquisitionFunction.bald):\n",
    "        return f'BatchBALD'\n",
    "    raise ValueError(vip_args)\n",
    "\n",
    "\n",
    "grouped_by = rl.groupby_dict(filtered,\n",
    "                             key_kv=rl.get_diff_args_key2text(\n",
    "                                 filtered, ['tag'] + rl.culling_fields))\n",
    "\n",
    "grouped_by = rl.groupby_dict(filtered,\n",
    "                             key_kv=key2text)\n",
    "\n",
    "\n",
    "pp.pprint(rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "grouped_by = rl.map_dict(\n",
    "    grouped_by,\n",
    "    v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(40, 45)))\n",
    "\n",
    "sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "#plt.title(\"EMNIST\")\n",
    "alp.plot_aggregated_groups(sorted_dict,\n",
    "                           show_num_trials=False,\n",
    "                           show_quantiles=False, show_thresholds=False)\n",
    "plt.axis([0, 0+280, 1/47, 0.5])\n",
    "acc_label_axes()\n",
    "plt.grid(True)\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "output_path = blackhc.notebook.original_dir + '/EMNIST_zero_initial_data.png'\n",
    "alp.plot_save(output_path, dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
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